Application of artificial neural networks for stress state analysis based on the photoelastic method

Abstract

The present article proposes an evolutionary development of the photoelasticity method for measuring stresses based on annular photoelastic sensors application along with stress pattern recording with the aid of a digital camera and its recognition using artificial neural networks. The analysis of the modern application of the photo- elasticity method for various problems within the theory of strength is presented. The principle of operation of photoelastic sensors based on the photoelasticity effect is considered. Optical patterns in an annular photoelastic sensor are presented for various values of the horizontal stress. The calculation of the stress state of the sensor for the following full-scale experiment has been performed, the estimate of the threshold conditions under which the sensor can be applied has been performed. As a result of a laboratory experiment, a dataset of 1500 isochromatic images has been assembled. A subspecies of a neural network, namely a convolutional neural network, has been applied as a machine learning algorithm. Different combination of models and optimizers have been employed. The application of downhole sensors for continuous monitoring of alterations in the rock mass stress state and the integration of this data into a digital field model based on Internet of Things technologies has been proposed.

Keywords

Artificial neural networks / Convolutional neural network / Photoelasticity / Polariscope

References

[1] Q.-Y. Zhang, Y. Zhang, K. Duan, C.-C. Liu, Y.-S. Miao, D. Wu, Large-scale geo- mechanical model tests for the stability assessment of deep underground complex under true-triaxial stress, Tunn. Undergr. Space Technol. 83 ( 2019) 577-591, https://doi.org/10.1016/j.tust.2018.10.011.

[2] L. Zheng, Y. Zuo, Y. Hu, W. Wu, Deformation mechanism and support technology of deep and high-stress soft rock roadway, Advances in Civil Engineering ( 2021), 6634299, https://doi.org/10.1155/2021/6634299.

[3] S. Jiang, G. Fan, Q. Li, S. Zhang, L. Chen, Effect of mining parameters on surface deformation and coal pillar stability under customized shortwall mining of deep extra-thick coal seams, Energy Rep. 7 ( 2021) 2138-2154, https://doi.org/10.1016/j.egyr.2021.04.008.

[4] K.H. Holmøy, B. Nilsen, Significance of geological parameters for predicting water inflow in hard rock tunnels, Rock Mech. Rock Eng. 47 ( 2014) 853-868, https://doi.org/10.1007/s00603-013-0384-9.

[5] X. Li, F. Gong, M. Tao, L. Dong, K. Du, C. Ma, Z. Zhou, T. Yin, Failure mechanism and coupled static-dynamic loading theory in deep hard rock mining: a review,J. Rock Mech. Geotech. Eng. 9 ( 2017), 767e782, https://doi.org/10.1016/j.jrmge.2017.04.004.

[6] Q. Qian, X. Zhou, Failure behaviors and rock deformation during excavation of underground cavern group for jinping I hydropower station, Rock Mech. Rock Eng. 51 (8) ( 2018) 2639-2651, https://doi.org/10.1007/s00603-018-1518-x.

[7] F. Li, Q. Zhang, W. Xiang, Mechanism of splitting failure for high sidewall cavern of hydropower station based on complex function and strain gradient, Energies 14 (18) ( 2021) 5870, https://doi.org/10.3390/en14185870.

[8] L. Hu, X.-T. Feng, Y.-X. Xiao, R. Wang, G.-L. Feng, Z.-B. Yao, W.-J. Niu, W. Zhang, Effects of structural planes on rockburst position with respect to tunnel cross- sections: a case study involving a railway tunnel in China, Bull. Eng. Geol. Environ. 79 ( 2020) 1061-1081, https://doi.org/10.1007/s10064-019-01593-0.

[9] Ø. Dammyra, B. Nilsena, J. Golleggerb, Feasibility of tunnel boring through weakness zones in deep Norwegian subsea tunnels, Tunn. Undergr. Space Technol. 69 ( 2017) 133-146, https://doi.org/10.1016/j.tust.2017.06.012.

[10] R. Liu, Y. Liu, D. Xin, S. Li, Z. Zheng, C. Ma, C. Zhang, Prediction of water inflow in subsea tunnels under blasting vibration, Water (Switzerland) 10 (10) ( 2018) 1336, https://doi.org/10.3390/w10101336.

[11] I.V. Biryuchev, A.B. Makarov, A.A. Usov, Geomechanical model of underground mine. Part I. creation, Gornyi Zhurnal 2020 (1) ( 2020) 42-48, https://doi.org/10.17580/gzh.2020.01.08.

[12] F. De Santis, V. Renaud, Y. Gunzburger, J. Kinscher, P. Bernard, I. Contrucci, In situ monitoring and 3D geomechanical numerical modelling to evaluate seismic and aseismic rock deformation in response to deep mining, Int. J. Rock Mech. Min. Sci. 129 ( 2020), 104273, https://doi.org/10.1016/j.ijrmms.2020.104273.

[13] S.A. Neverov, Types of orebodies on the basis of the occurrence depth and stress state. Part I: modern concept of the stress state versus depth, J. Min. Sci. 48 (2) ( 2012) 249-259, https://doi.org/10.1134/S1062739148020050.

[14] G.K. Li, S. Moon, Topographic stress control on bedrock landslide size, Nat. Geosci. 14 (5) ( 2021), https://doi.org/10.1038/s41561-021-00739-8.

[15] B. Rahimi, M. Sharifzadeh, X.-T. Feng, A comprehensive underground excavation design (CUED) methodology for geotechnical engineering design of deep underground mining and tunneling, Int. J. Rock Mech. Min. Sci. 143 ( 2021), 104684, https://doi.org/10.1016/j.ijrmms.2021.104684.

[16] A.A. Eremenko, Y.N. Shaposhnik, V.N. Filippov, A.I. Konurin, Development of scientific framework for safe and efficient geotechnology for rockbursthazardous mineral deposits in western Siberia and the far north, Gornyi Zhurnal 2019 (10) ( 2019) 33-39, https://doi.org/10.17580/gzh.2019.10.03.

[17] L.A. Krupnik, Y.N. Shaposhnik, A.I. Konurin, D.A. Shokarev, S.N. Shaposhnik, Improvement of support technology in Artemevsk mine of VOSTOKTSVETMET, J. Min. Sci. 53 (6) ( 2017) 1096-1102, https://doi.org/10.1134/S1062739117063173.

[18] Y.N. Shaposhnik, A.A. Neverov, S.A. Neverov, A.M. Nikol’sky, Assessment of influence of voids on phase II mining safety at artemievsk deposit, J. Min. Sci. 53 (3) ( 2017) 524-532, https://doi.org/10.1134/S1062739117032464.

[19] T.P. McGuire, D. Elsworth, Z. Karcz, Experimental measurements of stress and chemical controls on the evolution of fracture permeability, Transport Porous Media 98 (1) ( 2013) 15-34, https://doi.org/10.1007/s11242-013-0123-4.

[20] A.V. Leont'Ev, A.B. Makarov, A.Y. Tarasov, In situ stress state assessment in the Nurkazgan Mine, J. Min. Sci. 49 (4) ( 2013) 550-556, https://doi.org/10.1134/S1062739149040047.

[21] M.V. Kurlenya, V.D. Baryshnikov, D.V. Baryshnikov, L.N. Gakhova, V.G. Kachal’sky, A.P. Khmelinin, Development and improvement of borehole methods for estimating and monitoring stress-strain behavior of engineering facilities in mines, J. Min. Sci. 55 (4) ( 2019) 682-694, https://doi.org/10.1134/S106273911904604X.

[22] M. He, Q. Wang, Q. Wu, Innovation and future of mining rock mechanics, J. Rock Mech. Geotech. Eng. 13 ( 2021), https://doi.org/10.1016/j.jrmge.2020.11.005.

[23] M. Sishi, A. Telukdarie, Implementation of Industry 4.0 technologies in the mining industry - a case study, Int. J. Min. Miner. Eng. 11 ( 2020) 1, https://doi.org/10.1504/IJMME.2020.105852.

[24] X.-P. Zhou, J.-Z. Zhang, Q.-H. Qian, Y. Niu, Experimental investigation of progressive cracking processes in granite under uniaxial loading using digital imaging and AE techniques, J. Struct. Geol. 126 ( 2019) 129-145, https://doi.org/10.1016/j.jsg.2019.06.003.

[25] D.-F. Chen, X.-T. Feng, D.-P. Xu, Q. Jiang, C.-X. Yang, P.-P. Yao, Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling, Tunn. Undergr. Space Technol. 51 ( 2015), https://doi.org/10.1016/j.tust.2015.09.010.

[26] X. Zhang, H. Nguyen, X.-N. Bui, H.A. Le, T. Nguyen-Thoi, H. Moayedi, V. Mahesh, Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization, Tunn.Undergr. Space Technol. 103 ( 2020), https://doi.org/10.1016/j.tust.2020.103517.

[27] Y. Pu, D. Apel, W. Liu, H. Mitri, Machine learning methods for rockburst prediction-state-of-the-art review, Int. J. Min. Sci. Technol. 29 ( 2019), https://doi.org/10.1016/j.ijmst.2019.06.009.

[28] Y. Duan, Y. Shen, I. Canbulat, G. Si, Classification of clustered microseismic events in a coal mine using machine learning, J. Rock Mech. Geotech. Eng. 13 ( 2021), https://doi.org/10.1016/j.jrmge.2021.09.002.

[29] W. Jinqiang, P. Basnet, S. Mahtab, Review of machine learning and deep learning application in mine microseismic event classification, Mining of Mineral Deposits 15 ( 2021) 19-26, https://doi.org/10.33271/mining15.01.019.

[30] R. Bhatawdekar, H. Nguyen, J. Rostami, X. Bui, D. Jahed Armaghani, P. Ragam, E. Mohamad, Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network, J. Rock Mech. Geotech. Eng. 13 ( 2021), https://doi.org/10.1016/j.jrmge.2021.08.005.

[31] E. Isleyen, S. Duzgun, R. Carter, Interpretable deep learning for roof fall hazard detection in underground mines, J. Rock Mech. Geotech. Eng. 13 ( 2021), https://doi.org/10.1016/j.jrmge.2021.09.005.

[32] A. Krizhevsky, I. Sutskever, G. Hinton, ImageNet classification with deep convolutional neural networks, Neural Information Processing Systems 25 ( 2012), https://doi.org/10.1145/3065386.

[33] J.W. Phillips, TAM326-Experimental Stress Analysis. Board of Trustees, University of Illinois at Urbana-Champaign, 1998.

[34] G. Сloud, Optical Methods of Engineering Analysis, Cambridge University Press, 1988.

[35] A.J. Durelli, Introduction to Photo-Mechanics, Prentice hall, Englewood cliffs, New Jersey, 1965.

[36] J.M. Vasco-Olmo, B. Yang, M.N. James, F.A. Díaz, Investigation of effective stress intensity factors during overload fatigue cycles using photoelastic and DIC techniques, Theor. Appl. Fract. Mech. 97 ( 2018) 73-86, https://doi.org/10.1016/j.tafmec.2018.07.011.

[37] Y. Song, Experimental and numerical study on crack-arrest behaviours of rock-like SCDC specimens, Geotech. Geol. Eng. 38 ( 2020) 6795-6807, https://doi.org/10.1007/s10706-020-01469-1.

[38] Y. Nakajima, K. Yamada, K. Fukushima, R. Tanaka, K. Sekiya, Experimental study on the relationship between direction of crack propagation and thermal stress distribution in laser cleaving process for glass substrate, Journal of Laser Micro Nanoengineering 14 (2) ( 2019), https://doi.org/10.2961/jlmn.2019.02.0008.

[39] P. Liu, Y. Ju, G. Fu, Z. Ren, Visualization of full-field stress evolution during 3D penetrated crack propagation through 3D printing and frozen stress techniques, Eng. Fract. Mech. 236 ( 2020), 107222, https://doi.org/10.1016/j.engfracmech.2020.107222.

[40] G. Gao, D. Mao, R. Jiang, Z. Li, X. Liu, B. Lei, J. Bian, S. Wu, B. Fan, Investigation of photoelastic property and stress analysis for optical polyimide membrane through stress birefringence method, Coatings 10 ( 2020) 56, https://doi.org/10.3390/coatings10010056.

[41] W. Zhan, Y. Fang, C.V. Hoang, P. Huang, Instantaneous plane stress observation and numerical simulation during wear in an initial line contact, Tribol. Lett. 66 ( 2018) 90, https://doi.org/10.1007/s11249-018-1042-x.

[42] Y. Ju, H. Xie, X. Zhao, L. Mao, Z. Ren, J. Zheng, F.-P. Chiang, Y. Wang, F. Gao, Visualization method for stress-field evolution during rapid crack propagation using 3D printing and photoelastic testing techniques, Sci. Rep. 8 ( 2018) 4353, https://doi.org/10.1038/s41598-018-22773-0.

[43] Y. Ju, Z. Ren, X. Li, Y. Wang, L. Mao, F.-P. Chiang, Quantification of hidden whole- field stress inside porous geomaterials via three-dimensional printing and photoelastic testing methods, J. Geophys. Res. Solid Earth 124 ( 2019) 5408-5426, https://doi.org/10.1029/2018JB016835.

[44] F.G. Vieira, A.S. Scari, P.A.A. Magalh-aes Jr. J. S.R. Martins, C.A. Magalh-aes, Analysis of stresses in a tapered roller bearing using three-dimensional photoelasticity and stereolithography, Materials 12 ( 2019) 3427, https://doi.org/10.3390/ma12203427.

[45] B.R. Mose, D.K. Shin, J.H. Nam, Development of an experimental system to measure stresses in a bearing using photo-elasticity, Exp. Mech. 58 ( 2018) 437-447, https://doi.org/10.1007/s11340-017-0361-4.

[46] R. Ahmad, A.O. Hasan, H. Al-Rawashdeh, Photoelastic stress analysis of crankpin fillets of a crankshaft, J. Fail. Anal. Prev. 19 ( 2019) 476-487, https://doi.org/10.1007/s11668-019-00618-w.

[47] H. Zheng, D. Wang, R.P. Behringer, Experimental study on granular biaxial test based on photoelastic technique, Eng. Geol. 260 ( 2019), 105208, https://doi.org/10.1016/j.enggeo.2019.105208.

[48] Y. Ju, Z. Ren, L. Mao, F.-P. Chiang, Quantitative visualization of the continuous whole-field stress evolution in complex pore structures using photoelastic testing and 3D printing methods, Opt Express 26 (5) ( 2018) 6182-6201, https://doi.org/10.1364/OE.26.006182.

[49] J. Guo, B. Zhu, X. Liu, J. Luo, Z. Li, Study on the geo-stress loading and excavation unloading devices of the large-scale photoelastic model test for deep-buried tunnels, Hindawi, Shock and Vibration ( 2021), 1939505, https://doi.org/10.1155/2021/1939505.

[50] Y. Wang, G. Zheng, X. Wang, Development and application of a goaf-safety monitoring system using multi-sensor information fusion, Tunn. Undergr. Space Technol. 94 ( 2019), 103112, https://doi.org/10.1016/j.tust.2019.103112.

[51] E.R. Leeman, The determination of the complete state of stress in rock in a single borehole-Laboratory and underground measurements, Int. J. Rock Mech. Min. Sci. 5 (1) ( 1968) 31-38.

[52] N. Hast, The state of stresses in the upper part of the earth's crust, Eng. Geol. 2 (1) ( 1967) 5-17.

[53] Y.V. Tokovyy, Y.-H. Huang, C.-Y. Yen, C.-C. Ma, Analytical and experimental evaluation of stresses in elastic annuli subjected to three-point loading on the outer surface, Appl. Math. Model. 73 ( 2019) 442-458, https://doi.org/10.1016/j.apm.2019.04.027.